Product color emotional design based on 3D knowledge graph

To address the problem of fragmentation, integration difficulties in fuzzy front-end information, and ambiguity in color emotion knowledge representation and conversion within the current product color emotion design stage, this paper proposes a method based on 3D Knowledge Graph. The proposed approach aims to integrate product color emotion design into the “data knowledge + artificial intelligence” growth model, facilitating a beneficial knowledge output cycle driven by data. As for the proposed approach, it divides the design problem into three stages: in the first one, big data web crawler technology and natural language processing were employed to extract knowledge related to product color emotion design. In the second phase, the construction of a product color emotion imagery association model using the RotatE knowledge graph, thereby achieving knowledge fusion of product color emotion design, and all accumulated knowledge data is integrated into a 3D Knowledge Graph for visualization. Finally, in the third phase, the PageRank algorithm is applied to calculate primary and auxiliary color weight parameters, simulating the product color synergy mechanism and determining the color synergy effects. Then, realize the knowledge generation of product color emotional design. This approach combines the human visual experience feedback with extensive big data analysis, and accurately outputs the product color emotion design scheme that meets the user's emotional needs. Moreover, the effectiveness and applicability of the method are verified by an illustrative example involving modern machine tool.